000			01635nam a2200169 4500
008			171210b xxu||||| |||| 00| 0 eng d
020			_a9781506302768
082			_a519.536 _bOSB
100			_aOsborne, Jason W. _922959
245			_aRegression and Linear Modeling: Best Practices and Modern Methods
260			_bSage Publications, Inc. _c2017 _aNew Delhi
300			_a457p
500			_a1: A Nerdly Manifesto 2: Basic Estimation and Assumptions 3: Simple Linear Models With Continuous Dependent Variables: Simple Regression Analyses 4: Simple Linear Models With Continuous Dependent Variables: Simple ANOVA Analyses 5: Simple Linear Models With Categorical Dependent Variables: Binary Logistic Regression 6: Simple Linear Models With Polytomous Categorical Dependent Variables: Multinomial and Ordinal Logistic Regression 7: Simple Curvilinear Models 8: Multiple Independent Variables 9: Interactions Between Independent Variables: Simple moderation 10: Curvilinear Interactions Between Independent Variables 11: Poisson Models: Low-Frequency Count Data as Dependent Variables 12: Log-Linear Models: General Linear Models When All of Your Variables Are Unordered Categorical 13: A Brief Introduction to Hierarchical Linear Modeling 14: Missing Data in Linear Modeling 15: Trustworthy Science: Improving Statistical Reporting 16: Reliable Measurement Matters 17: Prediction in the Generalized Linear Model 18: Modeling in Large, Complex Samples: The Importance of Using Appropriate
600			_aRegression Analysis _924969
600			_aLinear Models _99168
942			_2ddc _cLB _k519.536 _mOSB
999			_c109286 _d109286